用图注意力网络提升医学图像无监督分割性能
UnSegMedGAT: Unsupervised Medical Image Segmentation using Graph Attention Networks Clustering
- 基于Dino-ViT特征，构建图像图结构并用GAT建模拓扑关系
- 在ISIC-2018和CVC-ColonDB上超越或媲美半监督方法MedSAM
- 适合数据稀缺场景下的医学图像分析研究者使用
监督分类依赖大量标注数据，推动研究向无监督方法发展，尤其在标注稀缺的医学图像分割领域。本文基于视觉变换器（ViT）的最新进展，提出一种基于预训练Dino-ViT的无监督分割框架。通过挖掘图像内部的图结构，引入基于模块度的损失函数与图注意力网络（GAT），有效捕捉图像内在拓扑关系，显著提升分割性能。在两个挑战性医学图像数据集ISIC-2018和CVC-ColonDB上，该方法表现达到当前最优，甚至超越或媲美现有半监督方法MedSAM（医学版Segment Anything Model）。本工作凸显了无监督方法在标注数据稀缺场景下推进医学图像分析的潜力。代码开源于[https://github.com/mudit-adityaja/UnSegMedGAT]。
原文摘要 · Abstract (English)
The data-intensive nature of supervised classification drives the interest of the researchers towards unsupervised approaches, especially for problems such as medical image segmentation, where labeled data is scarce. Building on the recent advancements of Vision transformers (ViT) in computer vision, we propose an unsupervised segmentation framework using a pre-trained Dino-ViT. In the proposed method, we leverage the inherent graph structure within the image to realize a significant performance gain for segmentation in medical images. For this, we introduce a modularity-based loss function coupled with a Graph Attention Network (GAT) to effectively capture the inherent graph topology within the image. Our method achieves state-of-the-art performance, even significantly surpassing or matching that of existing (semi)supervised technique such as MedSAM which is a Segment Anything Model in medical images. We demonstrate this using two challenging medical image datasets ISIC-2018 and CVC-ColonDB. This work underscores the potential of unsupervised approaches in advancing medical image analysis in scenarios where labeled data is scarce. The github repository of the code is available on [https://github.com/mudit-adityaja/UnSegMedGAT].
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